Pharma Analytics: Optimizing Pharmaceutical Value Chain with Big Data

Pharma Analytics: Optimizing the Pharmaceutical Value Chain with Big Data Analytics

The Pharmaceutical industry, traditionally, has been slow to adopt cutting-edge technologies. A complicated supply chain, a plethora of government compliances and regulations, tight profit margins, and burgeoning competition have been significant reasons hindering the switch to Pharma Analytics. But now, the pandemic has increased these obstacles and has more or less forced pharma companies to transform how they monitor, control, and optimize their supply chain.

Existing Challenges in the Pharmaceutical Supply Chain

  1. Drug Shortages and the inability to manage unexpected surges in demand. No mechanism in place to predict demands.
  2. Shrewd management of pharma inventory is not possible with traditional processes.
  3. There is no specific process to ensure the integrity and quality of the drug that reaches the patient’s hands.
  4. Supply chain managers do not have transparency over several parts of the supply chain because they aren’t digitized.
  5. There is no means to curb medical wastages or to study the environmental impact of the supply chain.
  6. No fall-back mechanism to mitigate the impact of natural or artificial disasters.

1. Avoiding Drug Shortages

2. Improve Visibility and Coordination

3. Combat Counterfeit products

4. Prevent Cold Chain Failures using Pharma Analytics

5. Minimizing Pharma Supply Chain Footprint

6. Minimizing Disruptions in Supply Chain

7. Disaster planning and crisis management using Pharma Analytics

Data Sources for Pharma Supply Chain Big Data Implementation

Pharma Analytics
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1. Product Data

2. Demand Data

3. Planning Data

4. Manufacturing Data

5. Inventory Data

6. Logistics Data

7. Supplier Data

8. Customer Data

9. Publicly Available Data

Pharma Analytics Services for Healthcare and Life Sciences

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